{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 语义分割和数据集\n",
    "\n",
    "最重要的语义分割数据集之一是[Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 4,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',\n",
    "                           '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')\n",
    "\n",
    "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "将所有输入的图像和标签读入内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 7,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def read_voc_images(voc_dir, is_train=True):\n",
    "    \"\"\"读取所有VOC图像并标注\"\"\"\n",
    "    txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',\n",
    "                             'train.txt' if is_train else 'val.txt')\n",
    "    mode = torchvision.io.image.ImageReadMode.RGB\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    features, labels = [], []\n",
    "    for i, fname in enumerate(images):\n",
    "        features.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'JPEGImages', f'{fname}.jpg')))\n",
    "        labels.append(torchvision.io.read_image(os.path.join(\n",
    "            voc_dir, 'SegmentationClass' ,f'{fname}.png'), mode))\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = read_voc_images(voc_dir, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "绘制前5个输入图像及其标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "imgs = train_features[0:n] + train_labels[0:n]\n",
    "imgs = [img.permute(1,2,0) for img in imgs]\n",
    "d2l.show_images(imgs, 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "列举RGB颜色值和类名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 12,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "查找标签中每个像素的类索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def voc_colormap2label():\n",
    "    \"\"\"构建从RGB到VOC类别索引的映射\"\"\"\n",
    "    colormap2label = torch.zeros(256 ** 3, dtype=torch.long)\n",
    "    for i, colormap in enumerate(VOC_COLORMAP):\n",
    "        colormap2label[\n",
    "            (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "    return colormap2label\n",
    "\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    \"\"\"将VOC标签中的RGB值映射到它们的类别索引\"\"\"\n",
    "    colormap = colormap.permute(1, 2, 0).numpy().astype('int32')\n",
    "    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "           + colormap[:, :, 2])\n",
    "    return colormap2label[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "例如"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]),\n",
       " 'aeroplane')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = voc_label_indices(train_labels[0], voc_colormap2label())\n",
    "y[105:115, 130:140], VOC_CLASSES[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "使用图像增广中的随机裁剪，裁剪输入图像和标签的相同区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def voc_rand_crop(feature, label, height, width):\n",
    "    \"\"\"随机裁剪特征和标签图像\"\"\"\n",
    "    rect = torchvision.transforms.RandomCrop.get_params(\n",
    "        feature, (height, width))\n",
    "    feature = torchvision.transforms.functional.crop(feature, *rect)\n",
    "    label = torchvision.transforms.functional.crop(label, *rect)\n",
    "    return feature, label\n",
    "\n",
    "imgs = []\n",
    "for _ in range(n):\n",
    "    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n",
    "\n",
    "imgs = [img.permute(1, 2, 0) for img in imgs]\n",
    "d2l.show_images(imgs[::2] + imgs[1::2], 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "自定义语义分割数据集类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class VOCSegDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"一个用于加载VOC数据集的自定义数据集\"\"\"\n",
    "\n",
    "    def __init__(self, is_train, crop_size, voc_dir):\n",
    "        self.transform = torchvision.transforms.Normalize(\n",
    "            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "        self.crop_size = crop_size\n",
    "        features, labels = read_voc_images(voc_dir, is_train=is_train)\n",
    "        self.features = [self.normalize_image(feature)\n",
    "                         for feature in self.filter(features)]\n",
    "        self.labels = self.filter(labels)\n",
    "        self.colormap2label = voc_colormap2label()\n",
    "        print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "    def normalize_image(self, img):\n",
    "        return self.transform(img.float() / 255)\n",
    "\n",
    "    def filter(self, imgs):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[1] >= self.crop_size[0] and\n",
    "            img.shape[2] >= self.crop_size[1])]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "                                       *self.crop_size)\n",
    "        return (feature, voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size = (320, 480)\n",
    "voc_train = VOCSegDataset(True, crop_size, voc_dir)\n",
    "voc_test = VOCSegDataset(False, crop_size, voc_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "origin_pos": 30,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 320, 480])\n",
      "torch.Size([64, 320, 480])\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,\n",
    "                                    drop_last=True,\n",
    "                                    num_workers=d2l.get_dataloader_workers())\n",
    "for X, Y in train_iter:\n",
    "    print(X.shape)\n",
    "    print(Y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "整合所有组件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def load_data_voc(batch_size, crop_size):\n",
    "    \"\"\"加载VOC语义分割数据集\"\"\"\n",
    "    voc_dir = d2l.download_extract('voc2012', os.path.join(\n",
    "        'VOCdevkit', 'VOC2012'))\n",
    "    num_workers = d2l.get_dataloader_workers()\n",
    "    train_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(True, crop_size, voc_dir), batch_size,\n",
    "        shuffle=True, drop_last=True, num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(\n",
    "        VOCSegDataset(False, crop_size, voc_dir), batch_size,\n",
    "        drop_last=True, num_workers=num_workers)\n",
    "    return train_iter, test_iter"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
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   "scroll": true
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 },
 "nbformat": 4,
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}